Task-based multi-process design synthesis with notification of transform signatures

ABSTRACT

A task-based multi-process design synthesis methodology relies on a plurality of child processes to assist a parent process in performing optimizations on an integrated circuit design. Objects from an integrated circuit design are grouped into subsets and assigned to child processes, with each child process performing a transform on each of the objects in the subset assigned to that child process and determining which of the objects in the subset are candidate objects for which performance of the transform has been successful. Each child process also provides result data to the parent process for each candidate object to reduce the overhead of the parent process when performing the transform on the candidate object. The result data, which may include, for example, a set of instructions or hints, may allow a parent process to take advantage of the efforts of the child process in performing the transform.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to the following applications filed on evendate herewith by Drumm et al.: U.S. Pat. No. ______, entitled“Task-Based Multi-Process Design Synthesis,” (ROC920100278US1) and U.S.Pat. No. ______, entitled “Task-Based Multi-Process Design SynthesisWith Reproducible Transforms,” (ROC920100280US1). The disclosures ofthese applications are incorporated by reference herein.

FIELD OF THE INVENTION

The invention is generally related to computers and computer software,and in particular, to computer software used in integrated circuitdevice design and synthesis.

BACKGROUND OF THE INVENTION

Integrated circuit devices, commonly known as chips, continue to becomemore powerful and complex as semiconductor manufacturing technologieshave advanced. Whereas early integrated circuit devices included fewerthan one hundred transistors, it is now common to integrate hundreds ofmillions of transistors into a single integrated circuit device. Thisincreased transistor count enables some operations that once requiredseveral integrated circuit devices to now be implemented in a singleintegrated circuit device, often providing greater performance at alower cost. For example, where previously a data processing system mightrequire separate integrated circuit devices for a microprocessor, amemory, a bus interface, and a memory controller, advances in chipdensity now permit all of these functions to be integrated into the sameintegrated circuit device. Such devices are typically known as “systemson a chip” due to the high level of integration they provide.

Increases in chip density have also significantly affected the designmethodologies used for integrated circuit chips. Rather than manuallylaying out individual transistors or logic gates in a design to obtain adesired logic function, typically the functional aspects of the designprocess are separated from the physical aspects. The functional aspectsof a design are typically addressed via a process known as a logicdesign, which results in the generation of a functional definition of acircuit design, typically defined in a hardware description language(HDL) such as VHDL or Verilog. An HDL representation of a circuit isanalogous in many respects to a software program, as the HDLrepresentation generally defines the logic or functions to be performedby a circuit design. Moreover, by separating logic design from physicallayout, functions are capable of being defined at a higher level ofabstraction. Many design methodologies rely on the concept ofhierarchical logic design, where circuits are defined in units andgrouped together in one or more parent containers representingcollections of units performing higher level functions.

In parallel with the creation of the HDL representation, a physicaldefinition of a circuit design is created typically via a layoutprocess, often referred to as integration, to essentially create a“floor plan” of logic gates and interconnects between the logic gatesrepresenting the actual physical arrangement of circuit elements on themanufactured integrated circuit. Automation tools have been developed toutilize predefined cells or blocks of complete circuits to assist withthe layout, thus eliminating the need to work with millions ofindividual logic gates. For example, synthesis tools have been developedto generate Random Logic Macro (RLM) blocks from an HDL representationof a design, whereby an individual laying out a design is merelyrequired to place the RLM blocks and connect them to one another tocomplete the circuit design. In addition, some designs incorporateblocks from off-the-shelf (OTS) logic blocks, which are reusable fromdesign to design.

Once a physical definition is created, testing and simulation of thedesign may be performed to identify any potential timing and/ormanufacturability issues, and once the design has been determined tomeet these requirements, the design may be utilized to manufactureintegrated circuits.

One manner of improving the performance of the tools used to designintegrated circuit devices is to rely on parallelization to leverage thecomputing power of multiple processors and/or computer systems.

For example, one class of tools conventionally used in integratedcircuit design are logic and physical synthesis tools. Synthesis is theprocess of transforming an input net list (a set of componentsinterconnected by a set of wires) of a design into an optimized net listunder certain desired optimization criteria. Physical synthesisadditionally involves the task of mapping the components of the net listto a physical image to realize the design. Optimizations involved inphysical synthesis typically consist of several complex steps and needto meet several constraints while achieving desired goals. It has beenfound that in many cases the physical mapping as well as the logicaltransformations are best dealt with simultaneously in order to achievebest results, a concept sometimes referred to as Placement DrivenSynthesis (PDS). This problem space for PDS is usually very largeleading to very long run times, so a significant need exists forreducing the run times of PDS and other logical and/or physicalsynthesis tools.

Conventional parallelization techniques in connection with integratedcircuit design usually involve partitioning a problem into fairlyindependent sub-problems and processing each of them separately. Thesesolutions of these sub-problems are then merged to obtain a solution forthe original problem. However, while there are many problems that lendthemselves to such partitioning it has been found that it is oftendifficult to partition the problem of physical and logical synthesiseither logically or physically, which renders the task ofparallelization even more difficult. This is because any PDS change madeto the design can affect much of the rest of the entire design, or atleast it is difficult if not impossible to tell a priori what effect aPDS change will have on other parts of the design.

Therefore, a significant need exists for an improved manner ofparallelizing physical and logical synthesis operations to reduceruntime.

SUMMARY OF THE INVENTION

The invention addresses these and other problems associated with theprior art by providing a task-based multi-process design synthesismethodology that relies on a plurality of child processes to assist aparent process in performing optimizations on an integrated circuitdesign. Objects from an integrated circuit design are grouped intosubsets and assigned to child processes, with each child processperforming a transform on each of the objects in the subset assigned tothat child process and determining which of the objects in the subsetare candidate objects for which performance of the transform has beensuccessful. Each child process also provides result data to the parentprocess for each candidate object to reduce the overhead of the parentprocess when performing the transform on the candidate object. Theresult data, which may include, for example, a set of instructions orhints, may allow a parent process to take advantage of the efforts ofthe child process in performing the transform, and avoid having toperform the transform from scratch, thereby reducing the workload of theparent process in performing the transform.

Therefore, consistent with one aspect of the invention, an integratedcircuit design is optimized by using a parent process and a plurality ofchild processes executing on one or more processors. The parent processgenerates a set of objects from the integrated circuit design upon whichto perform a transform. Each of the plurality of child processesperforms a transform on each of a subset of objects from the set ofobjects, including determining for each of the subset of objects whethersuch object is a candidate object for which performance of the transformhas been successful, for each candidate object for which performance ofthe transform has been determined to be successful, generates resultdata capable of being used to reduce an overhead of the parent processwhen performing the transform on such candidate object, and notifies theparent process of any candidate object from the subset of objects forwhich performance of the transform has been determined to be successful,including providing the result data generated for any such candidateobject to the parent process. The parent process then performs thetransform on each candidate object of which the parent process isnotified by the plurality of child processes using the result datagenerated for such candidate object.

These and other advantages and features, which characterize theinvention, are set forth in the claims annexed hereto and forming afurther part hereof. However, for a better understanding of theinvention, and of the advantages and objectives attained through itsuse, reference should be made to the Drawings, and to the accompanyingdescriptive matter, in which there is described exemplary embodiments ofthe invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of the principal hardware components in acomputer system suitable for implementing a task-based multi-processsynthesis consistent with the invention.

FIG. 2 is a flowchart illustrating an exemplary sequence of operationsfor a task-based design synthesis process capable of being implementedin the computer of FIG. 1.

FIG. 3 is a graph of number of processes vs. success ratio for a statictask-based multi-process design synthesis process.

FIG. 4 is a flowchart illustrating an exemplary sequence of operationsfor a dynamic task-based multi-process design synthesis process capableof being implemented in the computer of FIG. 1.

FIG. 5 is a graph of number of processes vs. success ratio for a dynamictask-based multi-process design synthesis process.

FIG. 6 is a graph of signature strength based upon optimum number ofprocesses.

FIG. 7 is a flowchart illustrating an exemplary sequence of operationsfor a task-based multi-process design synthesis process utilizingexternal partitioning.

FIG. 8 is a flowchart illustrating an exemplary sequence of operationsfor a task-based multi-process design synthesis process utilizingintegrated transform drivers and capable of being implemented in thecomputer of FIG. 1.

FIG. 9 is a flowchart illustrating an exemplary sequence of operationsfor another task-based multi-process design synthesis process capable ofbeing implemented in the computer of FIG. 1.

FIG. 10 is a flowchart illustrating a first implementation of theparallel task-based synthesis operation referenced in FIG. 9.

FIG. 11 is a flowchart illustrating a second implementation of theparallel task-based synthesis operation referenced in FIG. 9, utilizingtransform signatures.

FIG. 12 is a flowchart illustrating a third implementation of theparallel task-based synthesis operation referenced in FIG. 9, utilizingreproducible transforms.

DETAILED DESCRIPTION

A common technique used in optimization is basically trial-and-error. Aparticular optimization function (called a transform) is given an objectsuch as a pin or net. It examines the circuits in the vicinity of thisobject and decides if its transformation is possible. If so, it appliesthe transformation, analyzes the results, and then decides whether toleave the changes in place or back them out through an undo operation torevert the circuit to its original state.

Depending on the transform, the ratio of the number of successes to thenumber attempted ranges from fairly high (80% or better) to very low(less than 10%). Embodiments consistent with the invention make use ofthis characteristic to parallelize the optimization. In particular,objects from an integrated circuit design are grouped into subsets andassigned to child processes, with each child process performing atransform on each of the objects in the subset assigned to that childprocess and determining which of the objects in the subset are candidateobjects for which performance of the transform has been successful.

In one implementation, the child processes notify a parent process ofthose objects that qualify as candidate objects, so that the parentprocess only has to perform the transform on the candidate objects,thereby relieving the parent process from the overhead associated withperforming the transform on non-candidate objects for which thetransform has been determined by the child processes as not beingsuccessful.

In another implementation, the child processes provide result data,e.g., in the form of a transform signature, including data such asinstructions or hints that a parent process can use to reduce theoverhead required to perform the same transform that a child process hasperformed in the parent process.

In yet another implementation, reproducible transforms are used toenable design synthesis operations to be reproducible from run to run.Child processes may perform transforms on objects using the same initialstate of an integrated circuit design for each object, by undoing theresults of each transform, regardless of success. In addition, a parentprocess may track the return of status information from child processesand perform the transform on objects in a controlled sequence.

Typically, the child processes and the parent process use independentcopies of the integrated circuit design so that modifications to theindependent copies of the integrated circuit design by the childprocesses are not reflected in the copy of the integrated circuit designused by the parent process. Moreover, by providing the parent and childprocesses with independent copies of the integrated circuit design, theprocesses are able to perform the transform while having a complete viewof the integrated circuit design.

An integrated circuit design within the context of the invention mayinclude any number of different designs that represent various aspectsof an integrated circuit, e.g., a logical design, a physical design, ora combined logical/physical design such as used in connection with PDS.Objects from a design may include, for example, logic gates, pins,nodes, cells, blocks, multiplexers, register elements, latches, netsfrom a net list, etc., as well as more complex collections of suchentities.

A transform within the context of the invention may include practicallyany potential optimization that can be performed on an integratedcircuit design, and that is capable of being tested to determine whetherperformance of the transform improves the design. A transform istherefore a type of modification to a design in a controlled manner totransform the design in some fashion. As but one example, one type oftransform is an inverter removal optimization, where redundant pairs ofinverters are detected and removed. Another type of transform is abuffering optimization where buffers are inserted into long wires toimprove signal speed, or inserted to unload critical connections in thecase of overloaded sinks. Another type of transform is resizing, whichinvolves selecting a new cell from the technology cell library withdifferent timing and load driving characteristics (usually designed withdifferent sized transistors). Still another type of transform relates tophysical movement of objects, e.g., moving a gate from one location toanother to improve some criterion. This type of transform typically doesnot alter the logic connectivity of the circuit, and is strictly aphysical design change.

Therefore, a transform may include operations such as logicdecomposition, converting AND/OR gates to NAND gates and vice versa,resizing, inverter removal, buffer removal, buffer insertion, buffertree optimization, wire sizing, placement move, factoring anddecomposition, composition (e.g., combining two gates into one),redundancy removal, transduction, global flow, remapping, pin swapping,cloning, common term elimination, inverter pushing (e.g., variations ofDeMorgan operations). Some of the aforementioned transforms may be usedfor achieving more than one objective, e.g., resizing, which may be usedto reduce delay (i.e., to improve slack), to correct or improveelectrical violations (e.g., to meet some target slew or transitiontime), or to reduce area or power by sizing down gates that easily meetother criteria like timing or slew. In addition, some of theaforementioned transforms may be used typically (not necessarilyexclusively) only during logic synthesis (e.g., redundancy removal),whereas others are typically physical in nature (e.g., moving a box toimprove routing or timing or power, or resizing a wire). Practically anytransform that is potentially capable of optimizing or otherwiseimproving an integrated circuit design may be performed consistent withthe invention, so the invention is therefore not limited to theparticular transforms enumerated herein.

Other variations and modifications will be apparent to one of ordinaryskill in the art. Therefore, the invention is not limited to thespecific implementations discussed herein.

Hardware and Software Environment

Turning now to the drawings, wherein like numbers denote like partsthroughout the several views, FIG. 1 illustrates an apparatus 10 withinwhich task-based multi-process synthesis consistent with the inventionmay be performed. Apparatus 10 in the illustrated embodiment isimplemented as a server or multi-user computer that is coupled via anetwork 12 to one or more client computers 14. For the purposes of theinvention, each computer 10, 14 may represent practically any type ofcomputer, computer system or other programmable electronic device.Moreover, each computer 10, 14 may be implemented using one or morenetworked computers, e.g., in a cluster or other distributed computingsystem. In the alternative, each computer 10, 14 may be implementedwithin a single computer or other programmable electronic device, e.g.,a desktop computer, a laptop computer, a handheld computer, a cellphone, a set top box, etc., or in a high performance computer such as amassively parallel computer or supercomputer.

Computer 10 typically includes a central processing unit (CPU) 16including at least one hardware-based microprocessor coupled to a memory18, which may represent the random access memory (RAM) devicescomprising the main storage of computer 10, as well as any supplementallevels of memory, e.g., cache memories, non-volatile or backup memories(e.g., programmable or flash memories), read-only memories, etc. Inaddition, memory 18 may be considered to include memory storagephysically located elsewhere in computer 10, e.g., any cache memory in aprocessor in CPU 16, as well as any storage capacity used as a virtualmemory, e.g., as stored on a mass storage device 20 or on anothercomputer coupled to computer 10. Computer 10 also typically receives anumber of inputs and outputs for communicating information externally.For interface with a user or operator, computer 10 typically includes auser interface 22 incorporating one or more user input devices (e.g., akeyboard, a mouse, a trackball, a joystick, a touchpad, and/or amicrophone, among others) and a display (e.g., a CRT monitor, an LCDdisplay panel, and/or a speaker, among others). Otherwise, user inputmay be received via another computer or terminal.

For additional storage, computer 10 may also include one or more massstorage devices 20, e.g., a floppy or other removable disk drive, a harddisk drive, a direct access storage device (DASD), an optical drive(e.g., a CD drive, a DVD drive, etc.), a storage area network, and/or atape drive, among others. Furthermore, computer 10 may include aninterface 24 with one or more networks 12 (e.g., a LAN, a WAN, awireless network, and/or the Internet, among others) to permit thecommunication of information with other computers and electronicdevices. It should be appreciated that computer 10 typically includessuitable analog and/or digital interfaces between CPU 16 and each ofcomponents 18, 20, 22 and 24 as is well known in the art. Other hardwareenvironments are contemplated within the context of the invention.

CPU 16 desirable includes multiple processors, multi-core processors,and/or symmetric multithreading (SMT)-capable processors such that theworkload of a design synthesis routine may be distributed among multiplehardware threads of execution. Moreover, in some implementations, adesign synthesis routine may be distributed among multiple networkedcomputers. As will become more apparent below, the workload of a designsynthesis routine is handled by multiple processes, and as such, anyhardware and/or software environment capable of hosting multipleprocesses typically may be used.

Computer 10 operates under the control of an operating system 26 andexecutes or otherwise relies upon various computer softwareapplications, components, programs, objects, modules, data structures,etc., as will be described in greater detail below (e.g., synthesis tool28). Moreover, various applications, components, programs, objects,modules, etc. may also execute on one or more processors in anothercomputer coupled to computer 10 via network 12, e.g., in a distributedor client-server computing environment, whereby the processing requiredto implement the functions of a computer program may be allocated tomultiple computers over a network.

In general, the routines executed to implement the embodiments of theinvention, whether implemented as part of an operating system or aspecific application, component, program, object, module or sequence ofinstructions, or even a subset thereof, will be referred to herein as“computer program code,” or simply “program code.” Program codetypically comprises one or more instructions that are resident atvarious times in various memory and storage devices in a computer, andthat, when read and executed by one or more processors in a computer,cause that computer to perform the steps necessary to execute steps orelements embodying the various aspects of the invention. Moreover, whilethe invention has and hereinafter will be described in the context offully functioning computers and computer systems, those skilled in theart will appreciate that the various embodiments of the invention arecapable of being distributed as a program product in a variety of forms,and that the invention applies equally regardless of the particular typeof computer readable media used to actually carry out the distribution.Examples of computer readable media include tangible, recordable typemedia such as volatile and non-volatile memory devices (e.g., memory18), floppy and other removable disks, hard disk drives, magnetic tape,and optical disks (e.g., CD-ROMs, DVDs, etc.), among others.

In addition, various program code described hereinafter may beidentified based upon the application within which it is implemented ina specific embodiment of the invention. However, it should beappreciated that any particular program nomenclature that follows isused merely for convenience, and thus the invention should not belimited to use solely in any specific application identified and/orimplied by such nomenclature. Furthermore, given the typically endlessnumber of manners in which computer programs may be organized intoroutines, procedures, methods, modules, objects, and the like, as wellas the various manners in which program functionality may be allocatedamong various software layers that are resident within a typicalcomputer (e.g., operating systems, libraries, API's, applications,applets, etc.), it should be appreciated that the invention is notlimited to the specific organization and allocation of programfunctionality described herein.

Those skilled in the art will recognize that the exemplary environmentillustrated in FIG. 1 is not intended to limit the present invention.Indeed, those skilled in the art will recognize that other alternativehardware and/or software environments may be used without departing fromthe scope of the invention.

Task-Based Multi-Process Design Synthesis

As noted above, conventional parallelization techniques in connectionwith integrated circuit design usually involve partitioning a probleminto fairly independent sub-problems and processing each of themseparately. These solutions of these sub-problems are then merged toobtain a solution for the original problem. However, while there aremany problems that lend themselves to such partitioning it has beenfound that it is often difficult to partition the problem of physicaland logical synthesis either logically or physically, which renders thetask of parallelization even more difficult. This is because any changemade to a design can affect much of the rest of the entire design, or atleast it is difficult if not impossible to tell a priori what effect achange will have on other parts of the design.

Described herein are a number of different approaches forparallelization of placement driven synthesis (PDS), i.e., a sequence oflogic transformations (or transforms) that are aware of the physicalenvironment in which they operate. This set of transformations {t₁, t₂ .. . t_(m)} may be considered to be grouped into a sequence of commands(cmds) C={c₁, c₂ . . . c_(m)} where each c_(i) is a subset of transformsin the set T=t_(i1), t_(i2) . . . t_(ic)}. It will be appreciated,however, that while the disclosure hereinafter focuses on PDS, theprinciples of the invention may apply to other forms of physical and/orlogical design synthesis, as well as other optimization techniques forintegrated circuit designs. Therefore, the invention is not limited toPDS-related optimizations.

Embodiments consistent with the invention rely on task-basedmulti-process synthesis, which relies on task based partitioning. FIG.2, for example, shows a simplified flow 50 describing the operationsthat may be used to perform transformations on a design. A data model 52for the design, e.g., a net list, is first analyzed in block 54 toobtain a set of objects O={o₁, o₂, . . . o_(n)} 56 on which a commandscan potentially be applied to improve the design. This set of objectsmay include gates, nets, pins etc. and is alternatively referred to as alist of objects. While not required, a list may be ordered according tosome set of criteria. The criteria may be for example timing related,electrical property based, geography based, etc.

A command, which performs one or more transformations on an object, isthen applied on each object of the list in sequence (block 58). Duringthe application, the command is evaluated (block 60), and if the designimproves, control passes to block 62 to determine if more objects remainin the list, and if so, returns control to block 58 to apply the commandto the next object in the list. If the design deteriorates, however,block 64 undoes the command before passing control to block 62. Once allobjects in the list have been processed, block 62 passes control toblock 66 to cleanup and exit the routine.

In task based multi-process PDS each command is parallelized separately,and multiple commands may be utilized to optimize a design. Each commandworks on its own list of objects to optimize the design. The command mayinclude transforms that are quite complex to perform and thus quite timeconsuming. The list of objects O on which the command is attempted maybe very large requiring the command to be applied on many objects.However, while a command may be run on a large set of objects, theactual number of objects on which the command is successful can often bequite small. One may define success ratio s_(r) as the number of objectson which a command is successful divided by the total number of objectson which it is tried. If n is the number of objects on which a transformis attempted and n_(s) is the number of objects on which it is appliedsuccessfully, then

$s_{r} = {\frac{n_{s}}{n}.}$

In many instances, this success ratio s_(r) is relatively small and thatthe outcome of evaluations performed after application of the command isnegative more often than not. Unfortunately it is not possible to avoidthis computation as it is essential for determining those objects forwhich a command or transform results in a success. However, the flowillustrated in FIG. 2 does offer an enormous opportunity for run timeimprovement by parallelization. Evaluations can be performed in parallelusing multiple processes (referred to herein as child processes) inorder to extract a small set of objects on which success is relativelyassured. The command can be then run sequentially on this small set ofsuccessful objects by another process (referred to herein as a parentprocess, which may also function as a child process in some embodiments)thus greatly reducing the required computation time. The parallelizationmodels described hereinafter are predicated on this underlyingprinciple.

One PDS approach described hereinafter relies on design partitioning,where the design as well as the placement image space are partitioned upfront. This partitioning is based upon a coarse placement of the designon the image space. At the end of this step there are m image partitionseach with a unique design partition assigned to it. PDS may be runsimultaneously on each of these m image space design partitions tuples.Various synchronization points may be established to maintainconsistency of timing and other characteristics across image boundariesduring the PDS runs. After the m PDS runs are completed the results maybe combined by appropriately stitching across the image boundaries tocomplete the design.

Using the aforementioned task-based process, limits on the number ofprocesses such a system can tolerate can be analyzed. Discussed hereinare two different scenarios under which the analysis is performed. Inthe first it is assumed that the children complete their respectivetasks on all the objects in their respective subsets before the parentstarts processing the parent subsets. This case is referred to herein asstatic task based multi-process PDS. In the second it is assumed thatthe parent starts its processing as soon as the first “nugget”, orcandidate object, is obtained from the child; this case is referred toas dynamic task based multi-process PDS. In both these cases arelationship is established between the desired number of processes andthe success ratio. If the success ratio can be determined then it ispossible to set theoretical limits on the number of processors.

In the static approach it is assumed that all children complete theirevaluation on all the objects in their respective subsets before theparent starts processing the parent subsets. The advantage of thisapproach is that the order in which the parent processes successfulobjects received back from the children is the same as that in asequential approach. However this does not mean that the same subset ofobjects will be processed by both the sequential as well as themulti-process versions since children could have evaluated objectsdifferently from the sequential approach. This is due to the fact thattheir respective partitions may not have the same view of the design asthe sequential process. The order however may be maintained.

Let tc_(max), be the maximum of the computation times spent by the childprocesses, t_(o) be the parent overhead of managing the child processes,and t_(p) the time required by the parent to process the small set ofsuccesses. For simplicity start with the assumption that all childrenfinish processing before the parent works with the success set. Thetotal time required for the application of a command isT=tc_(max)+t_(o)+t_(p). If there are n objects to be processed and theaverage time required to process each object is t, then the timerequired to analyze application of the command on the entire set objectsis nt. If m child processes are used to perform this analysis and theload is distributed evenly amongst the children, then

${{tc}_{\max} \cong \frac{nt}{m}},$

and the time required to evaluate the command

${T_{c} = {t_{o} + \frac{nt}{m} + t_{p}}},$

where t_(p)=s_(r)nt is the time taken by the parent to reprocess thesuccessful objects. In the sequential case the time taken to processthese objects is T=nt. As long as T_(c) is substantially greater than T,parallelization will be effective. Typically starting new processes isextremely fast and this overhead can be neglected for the purposes ofthis analysis. The condition for parallelization to be effective istherefore

${\frac{nt}{m} + {s_{r}{nt}}} \leq {{nt}\mspace{14mu} {or}\mspace{14mu} m} \geq {\frac{1}{1 - s}.}$

On the higher end m is only limited by computing resources. FIG. 3 showsthis relationship, with a “sweet spot” for deciding the number ofprocesses also highlighted.

It is anticipated that in the final processing by the parent a very highsuccess ratio can be achieved. While this analysis provides theoreticallimits, it must be understood that due to practical considerations onlya portion of the region above the line is actually of value. Theseconsiderations include the overhead of managing parallel processes,memory overhead like copy on write, etc. Spawning a very large number ofprocesses could also lead to inefficiencies because as the childrenprocess their respective partitions they may create numerous copies ofthe design in memory leading to excessive paging.

In the dynamic approach objects are pipelined to children from theparent for evaluation and then information about successful evaluationsof objects is pipelined back to the parent. This results in aconsiderable savings in time at the expense of a greater deviation fromsequential PDS. The sequence in which objects are received by the parentdepends upon the rate at which different children fill the pipe and maydiffer from sequential PDS. A flow chart illustrating this approach isshown in FIG. 4.

In particular, a dynamic task-based multi-process PDS routine 70distributes subsets of objects from an object list 72 to a plurality ofchild processes 74. Each child process 74 outputs successful, orcandidate objects through associated pipes 76, which are then combinedat block 78. The combined candidate objects are then streamed via aconsolidated child pipe 80 for processing by the parent process in block82.

The dynamic process can be analyzed in a similar manner to the staticprocess. In this scheme all children will typically be working at fullcapacity until they have exhausted all the objects in the original listto be evaluated. However, since a large number of these evaluations maybe unsuccessful, the parent could remain idle until such a successfulobject is returned. Let N be the average number of objects in the pipecontaining potential candidates to be reevaluated by the parent. Onaverage N is also the number of objects processed by the parent afterall children terminate. As the number of children m is increases theparent pipe will fill up faster speeding up the child evaluationprocess. However, this will be offset by the time required by the parentto complete reevaluation as the queue size N at its input will begreater. One needs to strike an appropriate balance between these twoconflicting outcomes.

Assume that the evaluations of all the children have the same successrate. Let t_(e) be the time required to evaluate and update each object.Typically, the evaluation time involves performing the update, computinga metric and if the metric has deteriorated then reverting to thepre-transform state for the object. Computation of the metric is costlyand consumes the bulk of t_(e). The average arrival rate at each childpipe is s_(r)/t_(e) objects per unit time. One may assume that thisarrival rate is distributed according to the Poisson distribution. Theconsolidated child pipe is filled at m times that rate and hence thearrival rate at the input to the parent is

$\frac{{ms}_{r}}{t_{e}}.$

Due to the additive property of the Poisson distribution the arrivalrate at this pipe also follows the Poisson distribution. Assuming thatthe parent takes the same time t_(e) to evaluate the object, the servicerate is

$\frac{1}{t_{e}}.$

Assuming further that the service rate is greater than the arrival rate,that is

$\frac{1}{t_{e}} \geq \frac{msr}{t_{e}}$

and applying the single server queuing model the average length of thequeue in the consolidate pipe may be computed as

$N = {{\left( {1 - {ms}_{r}} \right){\sum\limits_{1}^{n}{k\left( {ms}_{r} \right)}^{k}}} = {\frac{{ms}_{r}}{1 - {ms}_{r}}.}}$

Using the above as a basis one may determine the desired number ofchildren. Note that the task based multi-process approach mustnecessarily take at least ns_(r)t_(e) time since the parent needs toreevaluate ns_(r) objects. The maximum amount of time that PDS can takeis when the children process all the unsuccessful objects first beforemaking successful objects available for the parent. This means that eachchild

needs to complete

${\frac{n}{m} - \frac{{ns}_{r}}{m}} = {\frac{n}{m}\left( {1 - s_{r}} \right)}$

evaluations on the average before embarking on the

$\frac{{ns}_{r}}{m}$

successful evaluations. As soon as a successful object is available theparent reevaluation process starts and the total time spent is

${\frac{n}{m}\left( {1 - s_{s}} \right)t_{e}} + {{ns}_{r}{t_{e}.}}$

The time required for the sequential system is nt. Therefore for themulti-process system to save time

${{\frac{{nt}_{e}}{m}\left( {1 - s_{r}} \right)} + {{ns}_{r}t_{e}}} \leq {{nt}_{e}\mspace{14mu} {or}\mspace{14mu} \frac{1}{m}\left( {1 - s_{r}} \right)} \leq {1 - {s_{r}\mspace{14mu} {or}\mspace{14mu} m}} \geq 1.$

On the other hand for the parent to be busy on the average there must beat least one item in the queue. If the average length of theconsolidated child queue (N) is at least one then the parent is alwaysreevaluating and the total process will on average complete inns_(r)t_(e) time. The arrival rate at the input to the parent is

${\lambda = \frac{{ms}_{r}}{t_{e}}},$

and the service rate

${\mu = \frac{1}{t_{e}}},$

leading to an arrival to service rate ratio of

$\rho = {\frac{\lambda}{\mu} = {{ms}_{r}.}}$

If one assumes that the arrival and service rates are Poissondistributions, using a single server queuing model (M/M/1) one sees that

$N = {\frac{\rho}{1 - \rho} = {\frac{{ms}_{r}}{1 - {ms}_{r}} \geq {1\mspace{14mu} {or}\mspace{14mu} m} \geq {\frac{1}{2\; s_{r}}.}}}$

This means that one needs

$\frac{1}{2\; s_{r}}$

processes to keep the system busy on average. Anything more than

$\frac{1}{2\; s_{r}}$

is even more likely to keep the system busy and less than

$\frac{1}{2\; s_{r}}$

is not as likely to keep the system busy. This simple analysis providedhere can be extended using other probability density distributionspd(t_(e)) of t_(e).

FIG. 5 shows a plot between the number of child processes and successratio. A good number of processes to use will be just above the line.The shaded region is the sweet spot to use in this case. This willensure that on the average the consolidated child pipe is kept full,while at the same time not wasting undue system resources to speedevaluation that will overwhelm the parent.

In the implementations already discussed, despite all the parallelismthat has been introduced in the evaluation of objects, the applicationof the transforms by the parent is essentially a sequential process. Inthe base implementation application of a transform by the parentincludes a complete reevaluation of the application of the transform onthe object. This is necessary because evaluations performed by the childcould have been made using a different state of the design (child'sview) and hence cannot be trusted by the parent.

In other implementations, however, it may be desirable to passadditional result data from a child to a parent to reduce the workloadof the parent in terms of evaluating an object. This result datareturned by the children about a specific object may be referred to as asignature. The children already evaluate the transform on each object.For each successful evaluation, a child can plot a course of actionsthat the parent should take to apply a transform instead of merelyreturning the success status. The process of executing these actions maybe referred to as signature actions or acting on a signature. One maydenote by t_(a) the time taken for a signature action. Any gain inperformance is predicated upon t_(a) being less than t_(e) sinceotherwise the actions would not have been suggested in the first place.In general t_(a) is very much smaller than t_(e). One may define thesignature strength

${ss} = {\frac{t_{e}}{t_{a}}.}$

This is also the inverse of the action to evaluation time ratio definedas

$s_{a} = {\frac{t_{a}}{t_{e}}.}$

Using the queuing model described above, the service rate of the parentmay be increased to

$\mu = {\frac{1}{t_{a}}.}$

Recalling that the arrival rate at the parent is

$\lambda = \frac{{ms}_{r}}{t_{e}}$

one may see that the arrival to service rate ratio may be designated as

$\rho = {\frac{\lambda}{\mu} = {{ms}_{r}{s_{a}.}}}$

Any extra penalty incurred by a child in creating the signature can beneglected since it is very small and is incurred only when theevaluation is successful. Since s_(a)<<1.0 the value of ρ is muchsmaller than when there was no action based retrieval. Using the sameexplanation as above, one may see that the parent can now support agreater number of children given by

$m \geq \frac{1}{2s_{r}s_{a}}$

greatly decreasing the turnaround time of the application. This isbecause the predominantly sequential part of the entire process has beenspeeded up considerably.

FIG. 6 shows the effect of signature strength on the optimum number ofprocesses. The x-axis shows the optimum number of processes forincreasing success ratio. Each plot shows on the y-axis the optimumnumber of processes for different signature strengths. The linearincrease in this number can be exploited to obtain considerable speed upin turnaround time.

Various implementations of task-driven multi-process PDS are envisioned.In two implementations discussed below, children return successfulobjects to the parent via a pipe. The two implementations differ in theway the object list is partitioned and assigned to the children.

In one implementation, referred to as external partitioning, the set ofobjects in the design are partitioned m ways. Each partition is assignedto a child that is then forked out. Each child sees the entire designbut only processes the object sub-list that is assigned to it. Thismethod leverages all existing and can be implemented with controls setup outside the source code implementing the driver. However, in order toensure that the load assigned to the various children is well balancedingenuity may need to be exercised in creating the partitions. The onusof load distribution is on the parent. It may be difficult in somecircumstances to cover all cases, leading to situations where the loadis not optimally distributed between the child processes.

FIG. 7, for example, illustrates an implementation 100 where a datamodel 102 is partitioned by a parent into subsets (block 104),generating a list of subsets 106. At block 110, the parent forks aplurality of child processes 112. Child processes 112 process theirrespective subsets, and output successful, or candidate objects 114,which are processed by the parent at block 116, by applying the desiredtransform to each candidate object. The result of the transform is thentested (block 118), and if unsuccessful, the transform is undone (block120). Once undone, or if the transform was successful, block 122 loopsback to block 116 until all candidate objects have been processed by theparent. Once all objects have been processed, the parent cleans up andexits in block 124, e.g., by killing all child processes.

Greater independence between the subsets typically ensures a greatersuccess ratio for the parent. This is because the effects of a commandsperformed by a child are not seen by any of the other children. Sinceeach child operates in its own environment without knowledge of theapplication of commands in other children, it is possible for the parentsubset to be slightly different from the set that would have beensuccessful in the single processor application of the command. Thiscould lead to different results between the single processor andmulti-processor cases. Since the spirit of the single processoralgorithm is maintained these differences will be marginal at worst.This method is also fault tolerant in that if any child should abortthen only the object set associated with that child will not beprocessed. The rest of the processing will proceed and synthesis willcomplete with perhaps slightly degraded quality.

FIG. 8 illustrates an alternate implementation 150 that relies onintegrated transform drivers (ITDs). In this scheme, parallelism isintegrated more tightly within the drivers. The parent pipelines itsobject list to the children and receives a parent list in a returnpipeline. The driver may be implemented either inside the transform oroutside the transform. In either case the parent creates a front endprocess (FEP) that accepts the object list and pipelines it to thechildren that it spawns. As a result any child that is free will pick upthe next object in the pipeline and continue evaluation as outlinedearlier. This ensures that the children are kept fully busy until thepipe is completely exhausted an all objects have been evaluated. Theobject list created by the parent is pipelined to the children fromwithin a driver that calls the transform.

As shown in FIG. 8, a design (network 152) is analyzed by the parent tocreate a list of objects (block 154), and the objects are grouped forlocality (block 156). The parent then sets up pipes (block 158), and inblock 160 starts an FEP 166, child processes 174 and rear end process(REP) 184.

FEP 166 takes a list of object groups 164 and retrieves objects (block168) and writes each object (block 170) to an object group pipe 172.Each child 174 gets objects from pipe 172 (block 176), processes thoseobjects by performing transforms thereon (block 178) and writes theresults to an associated child pipe 182 (block 180). REP 184 thenselects child pipes 182 (block 186) and retrieves results therefrom(block 188). REP 184 then writes results to a consolidated child pipe192 (block 190). The parent process then processes successful objects inblock 162.

It may be desirable to meet various constraints in order to optimize theperformance of the implementation illustrated in FIG. 8.

For example, in general the load is more evenly distributed amongst thechildren in the integrated driver implementation. The scheme may runsub-optimally when elements from the same group are distributed amongstdifferent children. This may cause different children to be working inconflicting ways on the same design. For example if the driver is timingbased and the objects are nets ordered by criticality, then it may bemore desirable to have the same child process all nets belonging to thesame critical path. Otherwise, it is possible that recommendations madeby children to the parent are conflicting resulting in unnecessary work.This can be remedied by grouping objects accordingly to ensure that suchconflicts are avoided. In some instances, however, grouping mayintroduce a load imbalance that must be carefully weighed in.

In addition, a primary purpose of the front end process is to eliminateany possibility of a deadlock. Besides, the front end process may bedesigned to allow for such sub-setting that enables improvement inquality of result (QOR). The object list may be pipelined as an objectsubset list to the children to meet such criteria. The FEP can also beused to reorder the object subset list and to perform any otherpreprocessing like filtering of the list that may be of value.

Further, after the children process each object that they pick up fromthe object group pipe, they send those objects on which the evaluationis successful back through REP to a return pipe that is processed by theparent. The REP can also be used to perform some post processing of theparent list in order to improve both the speed and quality of thetransform. The REP can also be used to create information that cansimplify the parent's evaluation of applying the transformation in orderto save computation time.

Each child may also provide a sequence of actions that the parent shouldtake for each object evaluated successfully by the child. For example,in the case of resizing of a gate, the child may recommend a modifiedgate size to be evaluated by the parent. The parent may then evaluateonly one gate size as opposed to trying every possible size for thegate. In other cases the child may recommend a set of nodes where timingmust be checked in order to accept a change that the child foundsuccessful. This leads to the design of a set of basic actions usingwhich feedback can be provided by the children to ease the work that theparent needs to perform.

In addition, in some implementations, reproducibility of results may beof paramount importance to designers. Reproducibility allows for thedesigner to try alternatives and revert back to a known state of thedesign if they do not prove to be effective. Furthermore, lack of suchconsistency from run to run will lead to uncertainty, which is oftenunacceptable.

The reasons for such inconsistency are twofold. Firstly, data read byeach child from the input pipe is may change from run to run. This datais determined by many factors that are environment dependent and notnecessarily program dependent. Secondly, the state of the design betweenone child and another tends to diverge over time because these childrenare not processing the same set of objects. As a result the parent mayprocess different sets of objects in one run compared to another run ofthe same program on the same data. Furthermore, the order in which theseobjects are processed may differ too. Since the results of PDS aredependent upon the objects that the parent processes as well as theorder in which they are processed, it may be desirable in someimplementations to find a way to maintain these inputs to the parentconsistent across different runs of PDS.

One manner of mitigating this problem is to ensure that child states donot change until some point of synchronization that is determined by theparent. This is done by not allowing children to apply any transform.They are only allowed to evaluate the transform. As a result, the stateof the design in each child is identical to the state of the design atthe time of last synchronization. Regardless of which child isevaluating the transform on an object the results will always match andthe set of objects processed by the parent will remain the same acrossruns.

In another manner, to ensure that the parent processes children in thesame order, the objects in the set created by the parent may be taggedwith a sequence number at the time of list creation. After evaluation,each child returns not only successes but also failures along with thesequence number and the result of the evaluation. Upon receiving theseresults, the parent can reorder the objects as they are received fromthe children and reevaluate and apply them in the reordered sequence.This will ensure that the order in which the objects are processed isthe same across runs as well. Note that the reordering and processingcan be combined to form a dynamic reordering mechanism to speed upcomputation time.

In addition, based upon their evaluation, in some implementationschildren can recommend a set of promising actions that are most likelyto bear good results for each object that they return as a success tothe parent. The parent merely evaluates these actions in this smallsubset instead of going through the drudgery of a complete reevaluationover the entire set of actions that are possible. Leveraging work doneby children in this manner effectively reduces the non-parallelcomputation of the run decreasing turnaround time.

Moreover, the process of creating and managing children has an overheadassociated with it. This overhead is due to requirements such asinitialization, generation of an object list, reporting, cleanup upontermination, effort involved in the forking process, etc. For transformsthat run for a relatively short amount of time these overheads result inparallelization taking more time than the sequential process. In someimplementations, therefore, transform collection may be used to mitigatethis problem.

Let TR={tr₁, tr₂, . . . , tr_(m)} be a collection of transforms andt_(i) be the amount of time taken to complete a transform tr_(i)χ TR.The time taken to complete the collection TR is T=t₁+t₂+ . . . +t_(m).Let the extra time due to the overhead of parallelization be t_(o). Thisoverhead is largely independent of the number of transforms performed orthe size of each transform. Furthermore, assume that parallelizationreduces the time t_(i) for a transform tr by a factor k. For theparallel run to take less time than the sequential run for tr, clearlyt_(o)+t_(i)/k≦t_(i), or t_(o)≦t_(i)(1−1/k). When t_(i) is small, therelation does not hold and parallelization is not justified. As t_(i)increases parallelization becomes more and more attractive. The time Tto process the collection TR is considerably larger that any t_(i),1≦i≦m. By choosing a collection TR such that t_(o)<<T(1−1/k)considerable gains can be obtained by parallelization.

In an implementation that relies on collection, transforms may be queuedrather than immediately executed. At various points, the queued set oftransforms may be collectively run on an object. That is, one list ofobjects can be created and multiple queued transforms may be run on eachobject in the list, accepting each of the transforms that wassuccessfully applied. Children may report back to the parent the set oftransforms that were successful for each object. This consolidates thesequential processes and increases the run time per object which lendsitself to the benefits of the herein-described parallel method,resulting in an overall run time improvement without compromising thequality of results.

Also, the ability to control child processes in the driver makes theimplementation of FIG. 8 relatively robust and more fault tolerant. If achild should abort, only those objects that it has picked up from thepipe but not processed to completion will be lost. The rest of any workthat may have been performed by this child will be picked up by otherchildren resulting in a degradation of run time. Any effect in thequality of results will be marginal if at all.

The number of children that are forked is typically based upon aninitial evaluation of the object list. A preliminary analysis of thislist can be performed to come up with an optimum number of children thatmust be processed. Once the process has commenced, in someimplementations the parent can start a monitoring process to control thenumber of children. If an estimate of the success ratio s_(r) forchildren is known prior to list processing, then using the formulationgiven earlier the number of children to be started can be computedaccurately based upon s_(r). However, this ratio is very much designdependent and while one can use the formulation as a guide line, it isdesirable for the monitoring process to track the queue length at theparent input to determine whether to start new processes if the parentis idle or whether to kill existing children if the parent isoverwhelmed.

The same monitoring process can also be used to keep track of anydivergence between the children. Such divergence is possible because aschildren apply commands to their respective object subsets, they changetheir local copies of the design and do not see changes made by otherchildren. In a robust system, information about such divergence can betransmitted back to the monitoring process which may periodically killand restart children to ensure that the divergence does not get out ofcontrol.

Another potential use for such a monitoring process would be to ensurethat memory utilization does not get out of hand. In programs such asPDS the portion of the design that is changed by any specific child mayspan several pages of memory. As a result the copy on write process thatis invoked to ensure that processes do not step on each other maypotentially result in excessive memory usage. The monitoring process maykeep track of memory usage and kill children without affecting thequality of the result in order to keep memory usage within limits. Thishas the dual advantage of controlling divergence while at the same timeresetting memory.

There may be additional opportunities available to speedup multi-processPDS that are dependent on platform characteristics. For example, memoryand cache sizes may be exploited to ensure that the number of childrenspawned is optimum.

Additional modifications will be apparent to one of ordinary skill inthe art having the benefit of the disclosure.

Turning now to FIG. 9, several implementations of task-basedmulti-process design synthesis are discussed hereinafter. FIG. 9, inparticular, illustrates a high level routine 200 for performingtask-based multi-process design synthesis on an integrated circuitdesign in a manner consistent with the invention.

Routine 200 may be used, for example, to optimize an integrated circuitdesign by applying one or more transforms to objects in an integratedcircuit design. Typically, routine 200 includes selection of objects(block 202) and selection of one or more transforms (block 204) to beperformed on the selected objects. Once objects and transforms areselected, parallel task-based design synthesis as described herein isperformed in block 206.

Typically, different optimizations are targeted to different types ofobjects, and as such, depending upon the type of optimization desired,object filtering may be performed to restrict the types of objectsrelevant to a particular type of optimization, e.g., logic gates forlogic decomposition, nets with negative slack, long wires with excessivedelay, etc.

In addition, the objects selected for processes can be ordered intolists, e.g., to prioritize objects for which optimization may be mostbeneficial. For example, it may be desirable to order objects based uponthe nets with the most slack, the wires with the greatest delay, thegeographic regions having poor characteristics, etc Otherwise, objectsmay be ordered based upon geographic location, or in other mannerssuitable for a given optimization. In other embodiments, no ordering ofobjects may be required.

Task-Based Multi-Process Design Synthesis With Notification of CandidateObjects

FIG. 10 illustrates an exemplary task-based multi-process designsynthesis routine 210 consistent with the invention. Routine 210 is usedto perform one or more transforms on a set of objects from an integratedcircuit design. Routine 210 begins in block 212 by generating a list ofobjects. The ordering of the list may or may not be significant and isgenerated in a particular way depending on the goals of the subjectoptimization. For instance, it may be a list of boxes (cells) in thedesign ordered from logic output to logic input. Next, in block 214, thelist of objects is grouped into subsets, e.g., with four objects pergroup. Next, in block 216, some number n child processes are generated,e.g., by forking the child processes. By virtue of the copy-on-writebehavior of fork, each child process begins with an identical butindependent view of the design. It will be appreciated that in otherembodiments, the client processes may already exist and/or the copy ofthe integrated circuit design in the parent process may need to beseparately copied to the client processes.

Next, in block 218, each child process requests and receives a group ofobjects from the original list. Then, as shown in block 220, a loop isinitiated to process each object in the group received by the childprocess. For each such object, the child process executes or performsthe transform on the object in block 222. Then, the child processtests/evaluates the results of the transform in block 224 to determinewhether performing the transform on the object was a success, i.e.,whether the transform improved or otherwise optimized the design. If thetransform succeeds, block 224 passes control to block 226 to return theidentity of the object (e.g., handle or name) to the parent to notifythe parent that performance of the transform on the object wassuccessful. Doing so identifies the object as a candidate object to theparent. Control then returns to block 220 to process additional objectspassed to the child.

Returning to block 224, if the result of the transform was notsuccessful, block 226 is skipped, and the parent is not notified of theobject. In addition, in some embodiments, it may be desirable to undothe transform on the child's copy of the design by passing control toblock 228. In other embodiments, however, no undoing of the transformmay be required.

For each candidate object returned to the parent process, the parentprocess performs the transform on the candidate object in block 230,thereby repeating the transform on the object on the parent's copy ofthe integrated circuit design. Block 232 then tests/evaluates theresults of the transform. If the transform is successful, control passesto block 240 to determine whether any objects remain to be processed,and if so, block 240 returns control to block 230 to process additionalobjects returned from the child processes. Once all objects have beenprocessed by the parent, block 240 passes control to block 242 to killthe child processes and otherwise clean up and terminate the routine.

If the transform is not successful, however, block 232 passes control toblock 234 to undo the transform. Next, block 236 determines whether itis desirable to regenerate the child process associated with theunsuccessful transform, and if so, block 236 passes control to block 238to kill and regenerate the child process, before passing control toblock 240. If not, block 238 is skipped.

Blocks 236 and 238, which may be omitted in some embodiments, addressthe situation where the independent copy of the integrated circuitdesign in a child process diverges from that of the parent process overthe course of the optimization. This divergence may be mitigated if itcauses too many invalid successes (those which do not executesuccessfully in the parent) by killing the offending child process, orall child processes, and regenerating it/them. The behavior of fork issuch that the new children are an identical snapshot of the parent, soregenerating a child process effectively updates the copy of theintegrated circuit design for the child to match the current copymaintained by the parent. Block 236, for example, may track a number ofinvalid candidate objects identified by a particular child process(i.e., candidate objects that the parent determines to be unsuccessful),and pass control to block 238 whenever that number meets some divergencemetric, e.g., when the number exceeds a threshold, the ratio of invalidcandidate objects to total candidate objects exceeds a threshold, etc.

Routine 210 provides run time reduction when applying a transform toobjects in a design by virtue of parallelizing the exploratory,nonproductive failing transformations. Typically, there is no speed upof the successful applications as they must still be performed by theparent. Thus, the lower the hit ratio, the more child processes may beused and the better the improvement. Further, if analysis for failuresis expensive, then the improvement is greater.

In addition, by executing the transform in the parent as normal (butonly for those objects where executing is likely to be successful), anychanges that degrade quality, due to the children not seeing the effectsof what other children are doing, will still be rejected by the parent.That is, the data from the children typically serve as suggestions, notcommands.

An additional benefit is that each child sees the entire design and assuch, there are no artificial boundaries introduced. Transforms thatrely on globally significant information will have that data available.In addition, if any child process dies or is killed, the parent processwill still typically be unaffected except for possibly missing somesmall number (depending on the group size) of potential optimizations.Further, children typically begin with a fresh, up-to-date view of thedesign.

Returning to FIG. 10, it may be desirable in some embodiments to queuetransforms (block 244) in lieu of performing them individually. For sometransforms, the amount of work to do for the transform may be too smalland finish too quickly. As an example, consider a set of transformsordered in a particular sequence. Say there are 20 such transforms. Eachtransform alone consumes a relatively short amount of time, say 60seconds on average. While each transform individually is reasonablyquick, the entire sequence is 20 minutes. When one of these transformsis parallelized, there is a certain amount of time that must be runsequentially, e.g., the initialization, generation of the list ofobjects, reporting, and cleanup. Plus, a small but noticeable amount ofoverhead may be added in forking the children, and collecting theresults. Performing the transforms individually therefore would notproduce a significant performance improvement over a non-parallelimplementation.

For such situations, it may be desirable to collect multiple transformsand perform them collectively for each object. With a “collect” modeenabled, for example, transforms may be queued rather than executed(block 244). At various points, the queued set of transforms areperformed all together. That is, one list of objects is created and allof the queued transforms are executed on each object, accepting each ofthe transforms that was successfully applied. Doing so consolidates thesequential processes and increases the run time per object, which lendsitself to the benefits of a parallel methodology. Thus, block 222 mayperform multiple transforms, and block 224 may test or evaluate each ofthe multiple transforms. Furthermore, block 226 may, in notifying theparent of a successful transform, also specify which transform that wassuccessful, so that the parent, in block 230, will perform only thosetransforms for which the child has determined were successful.

Task-Based Multi-Process Design Synthesis With Notification of TransformSignatures

While the implementation disclosed in FIG. 10 improves performance indesign synthesis operations, the parent process is required tore-evaluate a transform that was already evaluated once in a childprocess that returned the given object as a success, or candidateobject. If the level of effort required for the re-evaluation is highand/or the number of successful applications is high compared to thetotal number being evaluated, then the parent workload may beexcessively high and limit the potential improvement derived fromparallel operation.

It therefore may be desirable in some implementations to alter the datareturned by child processes to the parent. Rather than returning justthe target object and/or a success or failure flag, the child mayinstead return result data, e.g., in the form of a transform signature,that is usable by the parent process to reduce the overhead ofperforming the transform in the parent process. For example, the resultdata may include one or more instructions to be performed by the parentto replicate the child's result.

The result data may alternatively include one or more “hints,” e.g., tonarrow the workspace for the parent process by eliminating certainoptimizations that might otherwise be tried when performing a transform,i.e., so that the parent process avoids performing at least oneoperation that the parent process would otherwise perform whenperforming the transform. As but one example, if a buffer optimizationtransform tries several different buffer configurations, a hint from achild process may specify one or more buffer configurations that shouldbe tried by the parent process and/or one or more buffer configurationsthat the parent should not bother to try as they are known to not beproductive. As an example, a child process may suggest that a four-wayNAND be decomposed to two 2-way ANDs and a 2-way NAND.

A simple example is a resizing transform. For this transform, a logicgate may be mapped to a particular technology cell (e.g. a particularpower level of a 2-way NAND). The transform may rebind this gate to someor all of the possible power levels provided by the technology celllibrary appropriate for that gate (e.g., all possible 2-way NAND powerlevels). It notes the effects on timing and area, then selects the oneproviding the best result for a given criterion (e.g. best for timing).

In the implementation of FIG. 10, a child process might note that therewas an improvement possible for this particular gate. The parent wouldthen repeat the same process performed by the child, trying some or allof the possible power levels, and select the best one. Through providingresult data to the parent process, however, the child process may beable to indicate not only the particular gate to modify, but also theexact technology power level that was discovered to be best. The parentprocess would then be able to rebind the gate directly to this powerlevel, and without having to retry all of the other possible powerlevels.

More complex transforms may work similarly but may require in some casesa chain of instructions similar to an engineering change order (ECO).Result data may also include tests that the parent may use to make afinal go/no-go decision for each transformation. For the example above,such a test might be whether the timing did, in fact, improve. If not,the change would be discarded reverting to the original state. The testsare useful because the children are working independently and do not seechanges made by other children. Those changes may alter the logicnetwork sufficiently to alter the results of applying any specifictransformation.

FIG. 11 illustrates a parallel task-based design synthesis routine 250that reports result data from child processes to parent processes.Routine 250 begins in block 252 by generating a list of objects. Next,in block 254, the list of objects is grouped into subsets, e.g., withfour objects per group. Next, in block 256, some number n childprocesses are generated, e.g., by forking the child processes. By virtueof the copy-on-write behavior of fork, each child process begins with anidentical but independent view of the design.

Next, in block 258, each child process requests and receives a group ofobjects from the original list. Then, as shown in block 260, a loop isinitiated to process each object in the group received by the childprocess. For each such object, the child process executes or performsthe transform on the object in block 262. Then, the child processtests/evaluates the results of the transform in block 264 to determinewhether performing the transform on the object was a success, i.e.,improved or otherwise optimized the design. If the transform succeeds,block 264 passes control to block 266 to generate a transform signature,e.g., a set of instructions detailing how to replicate the transform onthe object. Next, block 268 returns the identity of the object (e.g.,handle or name) to the parent to notify the parent that performance ofthe transform on the object was successful. In addition, the transformsignature is returned to the parent to reduce the overhead for theparent process when performing the transform. Control then returns toblock 260 to process additional objects passed to the child.

Returning to block 264, if the result of the transform was notsuccessful, blocks 266-268 are skipped, and the parent is not notifiedof the object. In addition, in some embodiments, it may be desirable toundo the transform on the child's copy of the design by passing controlto block 270. In other embodiments, however, no undoing of the transformmay be required.

For each candidate object returned to the parent process, the parentprocess performs the transform on the candidate object in block 272,thereby repeating the transform on the object on the parent's copy ofthe integrated circuit design. In addition, the parent process uses theresult data in the transform signature to reduce the overhead ofrepeating the transform. For example, if the child process provides aset of instructions, the parent follows the instructions to repeat thetransform. Optionally, the parent may also perform tests specified inthe result data, either during or after applying the changeinstructions, and reject (by undoing) any changes that fail the tests.

Block 274 next tests/evaluates the results of the transform. If thetransform is successful, control passes to block 282 to determinewhether any objects remain to be processed, and if so, block 282 returnscontrol to block 272 to process additional objects returned from thechild processes. Once all objects have been processed by the parent,block 282 passes control to block 284 to kill the child processes andotherwise clean up and terminate the routine.

If the transform is not successful, however, block 274 passes control toblock 276 to undo the transform. Next, block 278 determines whether itis desirable to regenerate the child process associated with theunsuccessful transform, and if so, block 278 passes control to block 280to kill and regenerate the child process, before passing control toblock 282. If not, block 280 is skipped.

The implementation of FIG. 11 may improve performance compared to theimplementation of FIG. 10 in instances where the evaluation cost ishigh, perhaps due to many expensive choices that must be evaluated or toa complex and expensive algorithm needed to determine the proper action,or when the hit rate is high (the ratio of successes to evaluations).Such cases may swamp the parent with excessive workload, rendering theparent process a bottleneck on performance, so any reduction in workloadin the parent can relieve any bottlenecks that otherwise may result.

In one exemplary implementation, result data may incorporateinstructions, referred to herein as actions, defined as follows:

move_phys—move a gate to a new physical location

move_pin—move a pin to a new net

bind—change the technology cell binding for a gate

insert—insert a new gate

key—set a keyword on an object (gate, pin, or net)

test—perform various tests

This set of instructions may be used, for example, to generatesignatures for a transform that inserts buffers into paths to resolvetiming HOLD, or early mode, violations. To cover a broader range oftransforms, other actions may be defined.

A variation of this implementation involves returning not the exactinstructions or actions for the parent to perform but, instead, a set ofhints the parent may use to reduce the analysis effort. An example makesthis clearer. Suppose a transform is being performed to construct abuffer tree connecting a source to a set of sinks. Rather than providingthe exact configuration along with all the buffer or inverter technologycells used, a child process may return just the topology of the tree,e.g., which sets of sinks are driven from the same buffer, the number ofbuffering stages, etc. This allows the parent some freedom to make someof the decisions based on the state of the design (such as buffertechnology cells and the physical locations of the buffers). Since thedesign state is not identical to that of the child, this freedom expandsthe all-or-nothing approach described above while sacrificing some runtime performance.

Task-Based Multi-Process Design Synthesis With Reproducible Transforms

In some instances, the aforementioned implementations may be subject tovarying results from run to run due to the asynchronous nature ofparallel processing. As an example, suppose there are nine nets to beexamined (i.e., at which a transform will be attempted) labeled 1, 2, 3,4, 5, 6, 7, 8, and 9. If three child processes, A, B, and C, arestarted, child A might work on nets 1, 2, and 3, child B might work onnets 4, 5, and 6, and child C might work on nets 7, 8, and 9. On asubsequent run, three child processes, A′, B′, and C′, may again bestarted. In this case, child A′ might work on nets 1, 2, and 5, child B′might work on 3, 6, and 9, and child C′ might work on nets 4, 7, and 8.Because executing the transforms changes the state of the design, atransform that is successful at net 5 in child B might very well failwhen it is evaluated by child A′. Thus, the parent may receive net 5 asa success in the first case but as a failure in the second.

In addition, the parent receives successes and applies the transform atthose objects (nets in the example above) in the order they are sent bythe children. The computer load and other issues outside the scope ofthe optimization system can also affect this order. Since theapplication of a transform may affect the success or failure of asubsequent transform application, the state of the design at the end ofthe parallel process can vary somewhat.

In some instances, consistency is generally desired even overpotentially better results, because it provides a way to evaluatealternatives and understand cause and effect without the distraction ofvariation outside the scope of the designer's control.

These issues may be addressed through an alternate implementation thatvaries from the aforementioned implementations in two primary ways.

First, the parent may track the objects returned by the childrenrelative to the original ordered list and ensure the execution oftransforms will be done in the same order it sent them to the children.For instance if the parent receives object 6 but has not yet receivedobject 5, it may wait for object 5 before executing the transform onobject 6. This typically requires the child processes to return to theparent all objects, both successes and failures, to allow the parent toreadily determine when it may proceed.

Second, the child processes may be configured to no longer acceptchanges. That is, they evaluate each transform and always undo thechanges regardless of success/failure. After every transform trial, thedesign is always returned to its original state. This ensures everytransform is tried on each object using an identical starting point. Itdoesn't matter which child is testing a transform on any particularobject; the success or failure result will always be exactly the same.Further, the number of child processes has no bearing on the outcome.

Generally, there is no guarantee a particular transform that succeeds ina child for a particular object will also succeed in the parent. Theparent is, in fact, applying the transforms, which alters its state fromthat in which the child performed its test. However, the successfulapplication in a child will occur repeatedly for separate identical runsregardless of how many children are active or which child tests it. And,its success or failure in the parent will always be the same by virtueof the parent always executing the transforms on the given set ofobjects in the identical order. Thus, if a transform executing on object21 prevents the successful application of the transform on object 36,this will be the case for any number of runs provided all otherconditions are identical (no design, rule, or tool changes).

It may also be desirable for the parent to also provide a mechanism toallow it to avoid a deadlock situation due to the failure of a child.After waiting for some period, it can skip a missing object and processall remaining objects.

FIG. 12 illustrates a parallel task-based design synthesis routine 300that utilizes reproducible transforms. Routine 300 begins in block 302by generating a list of objects. Next, in block 304, the list of objectsis grouped into subsets, e.g., with four objects per group. Next, inblock 306, some number n child processes are generated, e.g., by forkingthe child processes. By virtue of the copy-on-write behavior of fork,each child process begins with an identical but independent view of thedesign.

Next, in block 308, each child process requests and receives a group ofobjects from the original list. Then, as shown in block 310, a loop isinitiated to process each object in the group received by the childprocess. For each such object, the child process executes or performsthe transform on the object in block 312. Then, the child processtests/evaluates the results of the transform in block 314 to determinewhether performing the transform on the object was a success, i.e.,improved or otherwise optimized the design.

Next, in block 316 the transform on the child's copy of the design isundone to restore the child's copy of the integrated circuit design toits initial state. Then, block 318 returns the identity of the object(e.g., handle or name) to the parent along with a success or failureflag to notify the parent whether performance of the transform on theobject was successful. Control then returns to block 310 to processadditional objects passed to the child.

For each candidate object returned to the parent process, the parentprocess records or logs the status of the object in block 320. Block 322then tests whether the status returned for the object is success orfailure. If failure, control returns to block 320 to log the results ofthe transforms for the other objects. If success, however, block 322passes control to block 324 to queue the object.

Block 326 then determines whether all preceding objects to the instantobject have been returned, and if not, passes control to block 328 todetermine whether all objects have been processed. If not, block 328returns control to block 320. If so, block 326 passes control to block330 to perform the transform on the object, thereby repeating thetransform on the object on the parent's copy of the integrated circuitdesign. Block 332 next tests/evaluates the results of the transform. Ifthe transform is successful, control passes to block 328 to processadditional objects. On the other hand, if not successful, control passesto block 334 to undo the transform, prior to passing control to block328. In addition, in some embodiments it may be desirable toadditionally perform the transform in sequence on each subsequent objectin the queue up to the next object that has not yet been received,undoing any unsuccessful transforms as necessary.

Once all objects have been processed by the parent, block 328 passescontrol to block 336 to determine whether any objects were missing,i.e., for which no status was returned by a child process. If not,control passes to block 340 to kill the child processes and otherwiseclean up and terminate the routine.

If missing objects do exist, however, block 336 passes control to block338 to perform the transform on all remaining objects in the queue,undoing any unsuccessful transformations, e.g., as discussed above inconnection with blocks 330-334. Control then passes to block 340 toterminate the routine.

It will also be appreciated that multiple transforms may be performed onobjects in a collective manner, as noted above. In such an embodiment,the status for each transform on each object can be separately trackedand used to ensure that the transforms are applied in the same,reproducible order.

Various manners of tracking and queuing objects returned from childprocesses may be used. For example, tracking and queuing may beimplemented using three arrays, a keyword, and a queue to do theordering.

The first array is an array of objects, and the second array is a statusarray, the latter of which is set to processed, or not processed, or isan index into an array of successes. All of the entries in the statusarray start out as not processed. The third array is an array ofsuccesses, and stores all of the objects associated with successfulchild attempts. There is a keyword, so that, given an object, thecorresponding index in the object and status arrays can be determined,and the queue contains the next success to be returned to the driver.There is also a current pointer, which stores the index of the nextobject needed in the sequence. This is the “leftmost” element of thearray that is marked as not processed.

When a parent driver does a read, if the queue is not empty, the queuedata is returned to the parent. If the queue is empty, and actual readis attempted.

READ_PROCESS:

Suppose a record comes back from the child. The object from the childdata is used to get an array index for the status array from theobject's keyword. If the child failed, the status for the index ismarked as processed. If the child succeeded, the information from thechild is put into the success array at the next available slot, S. Thestatus index for the object is set to S. If the value ofstatus_array[current_pointer] is no longer not processed, loop throughthe array until index of the next not processed entry is found and resetthe current pointer to that index. During the loop, an entry that isneither processed nor not processed will be an index into the storedsuccess array. This data will be pushed onto the queue and thecorresponding index set to processed. If the queue is not empty, the topelement from the queue is passed back to the driver parent.

END_PROCESS:

When end-of-file is found on the pipe and the queue is empty, thecurrent pointer is reset to 0. At this point, if everything is OK, allthe elements in the status queue should be marked as processed. An errormessage is given for anything marked not processed. Any index with apointer into the success table is pushed onto the queue and marked asprocessed. The queue is emptied as read requests continue from theparent driver. This is to ensure that every object in the object arraygets processed, one way or another.

An AGE_FACTOR may also be used when the difference between the index ofan object from the child, N, and the current pointer exceeds theAGE_FACTOR. When that happens, every success that has been stored isreleased in processing similar to the END_PROCESS. That is, storedsuccesses are queued and the indices are changed to processed. Thecurrent pointer is updated to be N. Processing proceeds as above, exceptfor the case where the object index, OI, for a read from the child isless than the current pointer. If this happens, the current pointer isset to OI and processing proceeds as above.

Therefore, it can be seen that embodiments consistent with the inventionprovide enhanced parallelism for design synthesis to reduce runtimestherefor. Various modifications may be to the illustrated embodimentsconsistent with the invention. Therefore, the invention lies in theclaims hereinafter appended.

1. A computer-implemented method of optimizing a logic design, themethod comprising: in a parent process, generating a set of objects fromthe logic design upon which to perform a transform; in each of aplurality of child processes: performing the transform on each of asubset of objects from the set of objects, including determining foreach of the subset of objects whether such object is a candidate objectfor which performance of the transform has been successful; for eachcandidate object for which performance of the transform has beendetermined to be successful, generating result data capable of beingused to reduce an overhead of the parent process when performing thetransform on such candidate object; and notifying the parent process ofany candidate object from the subset of objects for which performance ofthe transform has been determined to be successful, including providingthe result data generated for any such candidate object to the parentprocess; and in the parent process, performing the transform on eachcandidate object of which the parent process is notified by theplurality of child processes using the result data generated for suchcandidate object.
 2. The method of claim 1, wherein generating theresult data comprising generating a transform signature.
 3. The methodof claim 1, wherein the result data for a first candidate objectincludes at least one instruction, and wherein performing the transformon the first candidate object includes executing the at least oneinstruction.
 4. The method of claim 3, wherein the at least oneinstruction is selected from the group consisting of a move physicallocation instruction, a move pin instruction, a bind instruction, andinsert instruction, a key instruction and a test instruction.
 5. Themethod of claim 1, wherein the result data for a first candidate objectincludes at least one hint, and wherein performing the transform on thefirst candidate object includes using the at least one hint to avoidperforming at least one operation that would otherwise be performed whenperforming the transform on the first candidate object.
 6. The method ofclaim 1, wherein the parent process and each of the plurality of childprocesses uses an independent copy of the integrated circuit design,wherein performing the transform in the parent process includesperforming the transform using the independent copy of the integratedcircuit design for the parent process, and wherein performing thetransform in each child process includes performing the transform usingthe independent copy of the integrated circuit design for such childprocess such that performing the transform in each child process doesnot alter the independent copy of the integrated circuit design for theparent process.
 7. The method of claim 6, further comprising, in theparent process, forking the plurality of child processes, whereinforking the plurality of child processes generates the independent copyof the integrated circuit design for each of the plurality of childprocesses from the independent copy of the integrated circuit design forthe parent process.
 8. The method of claim 1, further comprising queuinga plurality of transforms, wherein performing the transform in eachchild process includes collectively performing the plurality oftransforms in each child process, and determining for each of the subsetof objects and for each transform among the plurality of transformswhether such object is a candidate object for which performance of suchtransform has been successful, and wherein performing the transform inthe parent process includes performing only those transforms on onlythose candidate objects for which performance of such transform on suchcandidate object has been determined to be successful by a child processfrom among the plurality of child processes.
 9. The method of claim 1,wherein the transform is selected from the group consisting of a logicdecomposition transform, an inverter removal transform, and a buffertransform.
 10. The method of claim 1, wherein generating the set ofobjects includes filtering the objects in the integrated circuit designbased upon the transform being performed.
 11. The method of claim 1,wherein performing the transform in the child process includes undoingthe transform in response to determining that performance of thetransform has not been successful.
 12. The method of claim 1, whereinperforming the transform on the candidate in the parent process includesdetermining whether performance of the transform on the candidate objecthas been successful, and if not, undoing the transform in the parentprocess.
 13. An apparatus, comprising: at least one processor; andprogram code configured upon execution by the at least one processor tooptimize a integrated circuit design using a parent process and aplurality of child processes executed by the at least one processor,wherein the parent process generates a set of objects from theintegrated circuit design upon which to perform a transform, whereineach of the plurality of child processes performs the transform on eachof a subset of objects from the set of objects, determines for each ofthe subset of objects whether such object is a candidate object forwhich performance of the transform has been successful, generates resultdata capable of being used to reduce an overhead of the parent processwhen performing the transform on such candidate object for eachcandidate object for which performance of the transform has beendetermined to be successful, and notifies the parent process of anycandidate object from the subset of objects for which performance of thetransform has been determined to be successful, and provide the resultdata generated for any such candidate object to the parent process, andwherein the parent process performs the transform on each candidateobject of which the parent process is notified by the plurality of childprocesses using the result data generated for such candidate object. 14.The apparatus of claim 13, wherein each child process is configured togenerate the result data in a transform signature.
 15. The apparatus ofclaim 13, wherein the result data for a first candidate object includesat least one instruction, and wherein the parent process is configuredto perform the transform on the first candidate object by executing theat least one instruction.
 16. The apparatus of claim 16, wherein the atleast one instruction is selected from the group consisting of a movephysical location instruction, a move pin instruction, a bindinstruction, and insert instruction, a key instruction and a testinstruction.
 17. The apparatus of claim 13, wherein the result data fora first candidate object includes at least one hint, and wherein theparent process is configured to perform the transform on the firstcandidate object using the at least one hint to avoid performing atleast one operation that would otherwise be performed when performingthe transform on the first candidate object.
 18. The apparatus of claim13, wherein the parent process and each of the plurality of childprocesses uses an independent copy of the integrated circuit design,wherein the parent process is configured to perform the transform usingthe independent copy of the integrated circuit design for the parentprocess, and each child process is configured to perform the transformusing the independent copy of the integrated circuit design for suchchild process such that performing the transform in each child processdoes not alter the independent copy of the integrated circuit design forthe parent process.
 19. The apparatus of claim 18, wherein the parentprocess is further configured to fork the plurality of child processes,wherein forking the plurality of child processes generates theindependent copy of the integrated circuit design for each of theplurality of child processes from the independent copy of the integratedcircuit design for the parent process.
 20. The apparatus of claim 13,wherein the parent process is configured to queue a plurality oftransforms, wherein each child process is configured to perform thetransform by collectively performing the plurality of transforms, anddetermining for each of the subset of objects and for each transformamong the plurality of transforms whether such object is a candidateobject for which performance of such transform has been successful, andthe parent process is configured to perform the transform by performingonly those transforms on only those candidate objects for whichperformance of such transform on such candidate object has beendetermined to be successful by a child process from among the pluralityof child processes.
 21. The apparatus of claim 13, wherein the transformis selected from the group consisting of a logic decompositiontransform, an inverter removal transform, and a buffer transform. 22.The apparatus of claim 13, wherein the parent process is configured togenerate the set of objects by filtering the objects in the integratedcircuit design based upon the transform being performed.
 23. Theapparatus of claim 13, wherein each child process is configured to undothe transform in response to determining that performance of thetransform has not been successful.
 24. The apparatus of claim 13,wherein the parent process is configured to determine whetherperformance of the transform on the candidate object has beensuccessful, and if not, undo the transform in the parent process.
 25. Aprogram product, comprising: a computer readable medium; and programcode stored on the computer readable medium and configured uponexecution to optimize a integrated circuit design using a parent processand a plurality of child processes executed by at least one processor,wherein the parent process generates a set of objects from theintegrated circuit design upon which to perform a transform, whereineach of the plurality of child processes performs the transform on eachof a subset of objects from the set of objects, determines for each ofthe subset of objects whether such object is a candidate object forwhich performance of the transform has been successful, generates resultdata capable of being used to reduce an overhead of the parent processwhen performing the transform on such candidate object for eachcandidate object for which performance of the transform has beendetermined to be successful, and notifies the parent process of anycandidate object from the subset of objects for which performance of thetransform has been determined to be successful, and provide the resultdata generated for any such candidate object to the parent process, andwherein the parent process performs the transform on each candidateobject of which the parent process is notified by the plurality of childprocesses using the result data generated for such candidate object.